Title: Query by Self. Abstract: Training with hard-to-obtain and therefore valuable data, improving generalization performance, and accelerating training speed are all challenging problems in machine learning community. Active learning , whose performance depends on its query strategy, is a powerful tool for these challenges. Unlike the famous Query by Committee strategy aimed at classification problem and dependent on a committee of student, our proposed query by self is suitable to both classification and regression problem and requires only a student,  which benefits from estimated output variance, the intermediate product of Kalman filtering optimization. This means larger scope of application and less requirement for computation and the number of data.  Besides, this strategy reduces training time and improves accuracy via filtration of similar data and better generalization.  We theoretically explain query by self strategy from the perspective of entropy. To verify effectiveness of query by self empirically, we conduct several experiments on two classical models in machine learning.