  The quantification of aleatoric and epistemic uncertainty in terms of
conditional entropy and mutual information, respectively, has recently become
quite common in machine learning. While the properties of these measures, which
are rooted in information theory, seem appealing at first glance, we identify
various incoherencies that call their appropriateness into question. In
addition to the measures themselves, we critically discuss the idea of an
additive decomposition of total uncertainty into its aleatoric and epistemic
constituents. Experiments across different computer vision tasks support our
theoretical findings and raise concerns about current practice in uncertainty
quantification.
