  Deep Learning sets the state-of-the-art in many challenging tasks showing
outstanding performance in a broad range of applications. Despite its success,
it still lacks robustness hindering its adoption in medical applications.
Modeling uncertainty, through Bayesian Inference and Monte-Carlo dropout, has
been successfully introduced for better understanding the underlying deep
learning models. Yet, another important source of uncertainty, coming from the
inter-observer variability, has not been thoroughly addressed in the
literature. In this paper, we introduce labels uncertainty which better suits
medical applications and show that modeling such uncertainty together with
epistemic uncertainty is of high interest for quality control and referral
systems.
