  Various strategies for active learning have been proposed in the machine
learning literature. In uncertainty sampling, which is among the most popular
approaches, the active learner sequentially queries the label of those
instances for which its current prediction is maximally uncertain. The
predictions as well as the measures used to quantify the degree of uncertainty,
such as entropy, are almost exclusively of a probabilistic nature. In this
paper, we advocate a distinction between two different types of uncertainty,
referred to as epistemic and aleatoric, in the context of active learning.
Roughly speaking, these notions capture the reducible and the irreducible part
of the total uncertainty in a prediction, respectively. We conjecture that, in
uncertainty sampling, the usefulness of an instance is better reflected by its
epistemic than by its aleatoric uncertainty. This leads us to suggest the
principle of "epistemic uncertainty sampling", which we instantiate by means of
a concrete approach for measuring epistemic and aleatoric uncertainty. In
experimental studies, epistemic uncertainty sampling does indeed show promising
performance.
