The standard active learning setting assumes a willing labeler, who provides labels on informative examples to speed up learning. However, if the labeler wishes to be compensated for as many labels as possible before learning finishes, the labeler may benefit from actually slowing down learning. This incentive arises for instance if the labeler is to be replaced by the ML model, once it is learned. In this paper, we initiate the study of learning from a strategic labeler, who selectively abstains from labeling to slow down learning. We first prove that strategic abstention can prolong learning, and propose novel complexity measures to analyze the query cost of the learning game. Next, we develop a near-optimal deterministic algorithm, prove its robustness to strategic labeling, and contrast it with other active learning algorithms. We also provide extensions that encompass other learning setups/goals. Finally, we characterize the query cost of multi-task active learning, with and without abstention. Our first exploration of strategic labeling aims to add to our theoretical understanding of the imitative nature of ML in human-AI interaction.