  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.
