Active learning is a method in machine learning where the computer picks which data to learn from, instead of just taking random samples. The goal is to make accurate predictions with as few labeled examples as possible. The Theory of Disagreement-Based Active Learning explains how this method works and why it's effective. By focusing on disagreements between different learning algorithms, researchers have found that active learning can lead to better results with less data. This research is helpful for anyone interested in understanding how computers can learn from data more efficiently.