  Deep learning has played a major role in the interpretation of dermoscopic
images for detecting skin defects and abnormalities. However, current deep
learning solutions for dermatological lesion analysis are typically limited in
providing probabilistic predictions which highlights the importance of
concerning uncertainties. This concept of uncertainty can provide a confidence
level for each feature which prevents overconfident predictions with poor
generalization on unseen data. In this paper, we propose an overall framework
that jointly considers dermatological classification and uncertainty estimation
together. The estimated confidence of each feature to avoid uncertain feature
and undesirable shift, which are caused by environmental difference of input
image, in the latent space is pooled from confidence network. Our qualitative
results show that modeling uncertainties not only helps to quantify model
confidence for each prediction but also helps classification layers to focus on
confident features, therefore, improving the accuracy for dermatological lesion
classification. We demonstrate the potential of the proposed approach in two
state-of-the-art dermoscopic datasets (ISIC 2018 and ISIC 2019).
