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 and epistemic uncertainty. We further discuss how they can be estimated in medical imaging. More importantly, we 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：人工智能系统在医疗领域用于疾病早期筛查具有重要临床价值。深度学习在医学影像中展现出巨大潜力，但AI系统的可靠性和可信赖性限制了其在涉及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结果的同时生成置信度评估方面发挥着关键作用。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可疑区域或为临床医生提供额外信息。本文综述了深度学习中的多种不确定性类型，包括偶然不确定性和认知不确定性，并进一步探讨了其在医学影像中的估计方法。更重要的是，我们回顾了近年来将不确定性估计融入医学影像的深度学习模型进展。最后，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方向。期望本综述能激发学界进一步兴趣，并为研究人员提供关于不确定性估计模型在医学影像应用的最新参考。