  Active domain adaptation has emerged as a solution to balance the expensive
annotation cost and the performance of trained models in semantic segmentation.
However, existing works usually ignore the correlation between selected samples
and its local context in feature space, which leads to inferior usage of
annotation budgets. In this work, we revisit the theoretical bound of the
classical Core-set method and identify that the performance is closely related
to the local sample distribution around selected samples. To estimate the
density of local samples efficiently, we introduce a local proxy estimator with
Dynamic Masked Convolution and develop a Density-aware Greedy algorithm to
optimize the bound. Extensive experiments demonstrate the superiority of our
approach. Moreover, with very few labels, our scheme achieves comparable
performance to the fully supervised counterpart.
