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：在医疗保健领域使用人工智能系统进行疾病早期筛查具有重要的临床意义。深度学习在医学影像中展现出巨大潜力，但人工智能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。这在医学影像中尤为重要，因为模型预测的不确定性可用于识别需关注的区域或为临床医生提供额外信息。本文回顾了深度学习中的各类不确定性，包括偶然不确定性和认知不确定性，并进一步探讨了如何在医学影像中估计这些不确定性。更重要的是，我们综述了近年来将不确定性估计融入医学影像的深度学习模型的最新进展。最后，我们讨论了深度学习用于医学影像的不确定性估计所面临的挑战和未来方向。希望本综述能激发学界更广泛的兴趣，并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资料。