The use of AI systems in healthcare for the early screening of diseases is of great clinical importance. Deep learning has shown great promise in medical imaging, but the reliability and trustworthiness of AI systems limit their deployment in real clinical scenes, where patient safety is at stake. Uncertainty estimation plays a pivotal role in producing a confidence evaluation along with the prediction of the deep model. This is particularly important in medical imaging, where the uncertainty in the model's predictions can be used to identify areas of concern or to provide additional information to the clinician. In this paper, we review the various types of uncertainty in deep learning, including aleatoric uncertainty, epistemic uncertainty, and out-of-distribution uncertainty, and we discuss how they can be estimated in medical imaging. We also review recent advances in deep learning models that incorporate uncertainty estimation in medical imaging. Finally, we discuss the challenges and future directions in uncertainty estimation in deep learning for medical imaging. We hope this review will ignite further interest in the community and provide researchers with an up-to-date reference regarding applications of uncertainty estimation models in medical imaging.

翻译：人工智能系统在疾病早期筛查中的临床应用具有重要价值。深度学习在医学影像领域展现出显著潜力，但人工智能系统的可靠性与可信度限制了其在直接关系患者安全的真实临床场景中的部署。不确定性估计在深度模型预测过程中发挥着关键作用，能够提供置信度评估。这在医学影像领域尤为重要——模型预测的不确定性可用于识别潜在风险区域，或为临床医生提供辅助诊断信息。本文系统梳理了深度学习中的各类不确定性，包括偶然不确定性、认知不确定性和分布外不确定性，并探讨了其在医学影像中的估计方法。同时，我们综述了近年来融合不确定性估计的深度学习模型在医学影像领域的最新进展。最后，我们讨论了医学影像深度学习不确定性估计面临的挑战与未来发展方向。本综述旨在激发学界进一步研究兴趣，为研究者提供关于不确定性估计模型在医学影像中应用的最新参考。