The performance of a predictive model is tightly coupled with the data used during training. While using more examples in the training will often result in a better informed, more accurate model; limits on computer memory and real-world costs associated with gathering labeled examples often constrain the amount of data that can be used for training. In settings where the number of training examples is limited, it often becomes meaningful to carefully see just which examples are selected. In active learning (AL), the model itself plays a hands-on role in the selection of examples for labeling from a large pool of unlabeled examples. These examples are used for model training. Numerous studies have demonstrated, both empirically and theoretically, the benefits of AL: Given a fixed budget, a training system that interactively involves the current model in selecting the training examples can often result in a far greater accuracy than a system that simply selects random training examples. Imbalanced settings provide special opportunities and challenges for AL. For example, while AL can be used to build models that counteract the harmful effects of learning under class imbalance, extreme class imbalance can cause an AL strategy to "fail," preventing the selection scheme from choosing any useful examples for labeling. This chapter focuses on the interaction between AL and class imbalance, discussing (i) AL techniques designed specifically for dealing with imbalanced settings, (ii) strategies that leverage AL to overcome the deleterious effects of class imbalance, (iii) how extreme class imbalance can prevent AL systems from selecting useful examples, and alternatives to AL in these cases.