Active learning is a form of machine learning that involves training algorithms to learn from data in an interactive manner. It combines the strengths of two different approaches - supervised learning and unsupervised learning, by allowing the algorithm to ask questions or request more information when it's unsure about something. This approach helps improve the accuracy of predictions made by machine learning models, as they are able to learn from new data that was not previously available during training. Active learning is particularly useful in scenarios where there is limited labeled data available for training, and can help reduce the amount of manual annotation required.