Abstract: Though widely used, most deep convolutional neural networks fail to capture prediction uncertainty, which can be crucial in scenarios such as automotive applications and disease diagnosis. Aleatoric and epistemic uncertainties have been proposed in the Bayesian deep learning framework for regression and classification, which require training images with unambiguous labels for success. Some situations do not have precise labels by nature, such as age estimation or lesion contour annotation by different physicians in the real world. Label distribution learning (LDL) has been proposed to account for the label ambiguity. However, uncertainty estimation has not been studied for LDL. This study presents a Bayesian deep label distribution learning (BLDL) to obtain the uncertainties of L