Approximate nearest neighbor (ANN) search is a fundamental problem in many areas such as data mining and machine learning. Due to the fact that massive computational resources are required for processing ANN search on large-scale data, data owners prefer to outsource their data to a third-party server which is responsible for data management and ANN query processing. However, if the server is not trustworthy or has been tampered with, the query result returned by the server may be incorrect. In this paper, we introduce a scheme that enables users to verify the correctness of ANN query result returned by the server. Specifically, we design a novel structure called guided tuples to construct a verifiable index structure (VIS), which not only guarantees the correctness of the query result, but also significantly reduces both the transmission and computational overhead of verification object (VO). Finally, we have conducted extensive experiments on real datasets to verify the effectiveness of our proposed scheme.