  Recent studies in active learning, particularly in uncertainty sampling, have
focused on the decomposition of model uncertainty into reducible and
irreducible uncertainties. In this paper, the aim is to simplify the
computational process while eliminating the dependence on observations.
Crucially, the inherent uncertainty in the labels is considered, the
uncertainty of the oracles. Two strategies are proposed, sampling by Klir
uncertainty, which tackles the exploration-exploitation dilemma, and sampling
by evidential epistemic uncertainty, which extends the concept of reducible
uncertainty within the evidential framework, both using the theory of belief
functions. Experimental results in active learning demonstrate that our
proposed method can outperform uncertainty sampling.
