Anomaly detection (AD) in an unsupervised manner has become the go-to approach in applications where data labeling proves problematic. However, these approaches are not completely unsupervised, since they rely on the weak knowledge of the dataset distribution into anomalous and anomaly-free subsets and typically require post-training threshold calibration in order to perform AD. Yet, they do not take advantage of available positive samples during training. In contrast, fully supervised approaches have proven to be more accurate and more efficient; however, they require a sufficient number of anomalous images to be labeled on a per-pixel level, which represents a labor-intensive task. In this article, we propose a hybrid approach that utilizes the best of both worlds. We use an unsupervised approach to build a model for generating pseudo labels, followed by a supervised approach to increase the robustness of AD. Moreover, we extend this approach with an active learning schema that results in learning with mixed supervision. We achieve several improvements, i.e., the utilization of available positive image samples, improved AD performance, and the retention of real-time performance. The proposed approach yields results that are comparable to the fully supervised approach, and at the very least, reduces the number of required labeled anomalous samples.