Active learning is a learning paradigm that actively acquires extra information with an "effort" for a certain "gain" when building learning models. This paper unifies the effort and gain by studying active learning in the cost-sensitive framework. The major advantage of studying active cost-sensitive learning aims at the business goal of minimizing the total cost directly, thus the potential applications of the proposed methods are significant. We first study a simple random active learner "buying" additional examples at random in order to reduce the total cost of example acquisition and future misclassifications. Then we propose a novel pool-based cost-sensitive active learner "buying" labels of unlabeled examples in a pool. We evaluate our new cost-sensitive active learning algorithms and compare them to previous active cost-sensitive learning methods. Experiment results show that our pool-based cost-sensitive active learner requires a fewer number of examples yet it produces a smaller total cost compared to the previous methods.