  In this paper we propose a novel method which leverages the uncertainty
measures provided by Bayesian deep networks through curriculum learning so that
the uncertainty estimates are fed back to the system to resample the training
data more densely in areas where uncertainty is high. We show in the concrete
setting of a semantic segmentation task (iPS cell colony segmentation) that the
proposed system is able to increase significantly the reliability of the model.
