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First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. In this paper, the authors proposed a new active learning algorithm, which avoids the disagreement coefficient in the argument and label complexity. The proposed label complexity is also slight tighter than existing algorithm. Given a confidence-rated predictor with guaranteed error, the authors show how to use it to construct an active label query algorithm consistent in the agnostic setting. A novel confidence-rated predictor with guaranteed error that applies to any general classification problem is also proposed. They show that this predictor is optimal in the realizable case, in the sense that it has the lowest abstention rate out of all predictors that guarantee a certain error.