The integration of deep learning into medical diagnostics has heralded a new era of data-driven healthcare，demonstrating profound potential in tasks ranging from radiological image interpretation and pathological slide analysis to genomic sequencing and clinical outcome prediction. These advanced computational models have achieved，and in some cases even surpassed，human-level performance on various well-defined tasks，promising to enhance diagnostic accuracy，streamline clinical workflows，and facilitate personalized medicine. However，the remarkable success of these models is fundamentally predicated on an implicit assumption： the availability of large-scale，high-quality，comprehensively annotated datasets. This assumption is in stark contrast with the reality of clinical data，which are intrinsically complex and challenging. Real-world medical data are typically characterized by scarcity，heterogeneity，and imbalance. The deployment of conventional deep learning models，which often behave as black boxes in such complex data environments，poses critical risks. These models are notoriously prone to producing overconfident yet erroneous predictions and lack a mechanism to express their uncertainty or to reliably signal when they are operating outside their domain of expertise. In the high-stake context of clinical decision-making，where a single incorrect prediction can have dire effects on patient safety，this lack of trustworthy uncertainty awareness is a critical barrier to the responsible adoption of artificial intelligence （AI）. This review is motivated by the urgent need to address the challenge of real-world complex data in medical AI. We systematically structure the landscape of data complexities into three principal categories，which form the organizational framework of our analysis. The first one is data scarcity，which is common in rare diseases，underrepresented populations，and emerging pathologies； it leads to overfitting and poor generalization. The second one is data heterogeneity，which includes label noise，feature noise，multimodal fusion conflicts，and out-of-distribution （OOD） samples. The third one is data imbalance，where the dominance of normal over abnormal cases biases models toward the majority class，undermining their sensitivity for rare pathologies. A new paradigm—one that goes beyond mere prediction to incorporate principled uncertainty quantification—is required to address these challenges. This review centers on evidential deep learning （EDL），an emergent and powerful framework designed for this purpose. Unlike traditional models that output a single point-estimate probability via a softmax function，EDL reframes classification by placing a high-order probability distribution over the categorical distribution of classes. The core contribution of this review is that it provides a systematic，in-depth analysis of how the EDL framework and its associated uncertainty metrics can be specifically utilized to mitigate the problems arising from real-world complex medical data. We meticulously map EDL-based solutions to our three-pronged taxonomy of data challenges. For data scarcity，EDL models naturally express high epistemic uncertainty when making predictions for classes with insufficient training evidence，thereby providing a quantitative and actionable signal that the model’s output should be treated with caution. For data heterogeneity，EDL offers robust solutions. In cases of data noise and multimodal conflicts，the conflicting or low-quality evidence is reflected in the parameters of the learned Dirichlet distribution，leading to high overall uncertainty and preventing an overconfident but incorrect conclusion. EDL is particularly effective for OOD detection； an OOD sample，by definition，lacks supporting evidence in the training data，causing a well-trained EDL model to produce a prediction with high epistemic uncertainty and a“vacuous”evidence vector. This situation enables the model to effectively“abstain”from making a high-risk prediction and instead refer the case to a human expert. In the context of data imbalance，although not a direct remedy，EDL provides a more nuanced view than standard outputs. It can reveal when a model is uncertain about its predictions for minority classes，even if the predicted probability is low，thus helping identify instances that require further scrutiny. This study，therefore，provides a comprehensive and structured survey of the current state of the art in applying EDL to medical image analysis under real-world complex data conditions. We synthesize the disparate literature into a coherent conceptual framework，elucidating the underlying principles，core methodologies，and practical applications of EDL in this critical domain. In conclusion，we posit that evidential deep learning represents a pivotal step toward building safe，reliable，and trustworthy AI systems for medical applications. However，the field is still nascent and faces several challenges that open up avenues for future research. These challenges include the need for robust theoretical foundations for evidence modeling，improving the computational scalability of EDL methods，and developing techniques for effective uncertainty calibration to ensure that the quantified uncertainty meaningfully corresponds to the true likelihood of error. The clinical translatability of uncertainty needs to be explored，with focus on how to present uncertainty information to clinicians in an intuitive and actionable manner. Moreover，large-scale，prospective clinical validation studies are imperative to move beyond retrospective dataset benchmarks and rigorously assess the real-world effect and safety of EDL-based diagnostic systems. This review aims to serve as a valuable resource and roadmap，inspiring further research to bridge the gap between the theoretical promise of EDL and its practical deployment in future clinical workflows. The codes link： https://github.com/oscarab/Medical-EDL.