Active Learning is a machine learning paradigm where the model selectively queries an oracle (e.g., a human annotator) for labels on the most informative or uncertain data points. This approach helps improve model performance with fewer labeled instances by focusing on data that maximizes learning efficiency. Active learning is commonly used when obtaining labeled data is costly or time-consuming, as in medical diagnosis, image annotation, or natural language processing tasks. The process iteratively refines the model by training on an expanding set of labeled examples, ultimately improving accuracy with minimal manual annotation.