Title: On the Geometry of Deep Bayesian Active Learning. Abstract: We present  geometric Bayesian active learning by disagreements (GBALD), a framework that performs BALD on its geometric interpretation interacting with a deep learning model. There are two main components in GBALD: initial acquisitions based on core-set construction and model uncertainty estimation with those initial acquisitions. Our key innovation is to construct the core-set on an ellipsoid, not typical sphere, preventing its updates towards the boundary regions of the distributions. Main improvements over BALD are twofold: relieving sensitivity to uninformative prior and reducing redundant information of model uncertainty. To guarantee the improvements, our generalization analysis proves that, compared to typical Bayesian  spherical interpretation, geodesic search with ellipsoid can derive a tighter lower error bound and achieve higher probability to obtain  a nearly zero error. Experiments on acquisitions with several scenarios demonstrate that, yielding slight perturbations to noisy and repeated samples,  GBALD further achieves significant accuracy improvements  than BALD, BatchBALD and other baselines.