  Active domain adaptation (DA) aims to maximally boost the model adaptation on
a new target domain by actively selecting limited target data to annotate,
whereas traditional active learning methods may be less effective since they do
not consider the domain shift issue. Despite active DA methods address this by
further proposing targetness to measure the representativeness of target domain
characteristics, their predictive uncertainty is usually based on the
prediction of deterministic models, which can easily be miscalibrated on data
with distribution shift. Considering this, we propose a \textit{Dirichlet-based
Uncertainty Calibration} (DUC) approach for active DA, which simultaneously
achieves the mitigation of miscalibration and the selection of informative
target samples. Specifically, we place a Dirichlet prior on the prediction and
interpret the prediction as a distribution on the probability simplex, rather
than a point estimate like deterministic models. This manner enables us to
consider all possible predictions, mitigating the miscalibration of unilateral
prediction. Then a two-round selection strategy based on different uncertainty
origins is designed to select target samples that are both representative of
target domain and conducive to discriminability. Extensive experiments on
cross-domain image classification and semantic segmentation validate the
superiority of DUC.
