Title: Deep Active Anomaly Detection With Diverse Queries. Abstract: Selecting informative data points for expert feedback can significantly improve the performance of anomaly detection in various contexts, such as medical diagnostics or fraud detection. In this paper, we determine a set of conditions under which the ranking of anomaly scores generalizes from labeled queries to unlabeled data. Inspired by these conditions, we propose a new querying strategy for active anomaly detection that leads to systematic improvements over current approaches for this problem. It selects a diverse set of data points for labeling, achieving high data coverage with a limited budget. These labeled data points provide weak supervision to the unsupervised anomaly detection problem. However, correctly identifying anomalies requires an estimate of the fraction of anomalies in the data. We show how this anomaly rate can be estimated from the query set by importance-weighting, removing the associated bias due to the non-uniform sampling procedure. Extensive experiments on image, tabular, and video data sets show that our approach results in state-of-the-art active anomaly detection performance.