The verification problem comes with many applications and requires human efforts. Machine learning can help reduce human efforts spent on verification. By combining the learning and verification stages in a verification problem, we formalize the needs as a new problem called interactive verification. The problem allows an algorithm to flexibly use the limited human resource on learning and verification together. We propose to adopt upper confidence bound (UCB) algorithm, which has been widely used for the contextual bandit, to solve the interactive verification problem. Experiment results demonstrate that UCB has superior performance on interactive verification on many real-world datasets.