Active learning is a training approach where the model doesn’t just passively consume labeled data - it actively chooses which unlabeled examples would be most valuable to label next. By prioritizing samples the model is uncertain about or that best cover the data space, you can reach a target accuracy with far fewer labeled examples (and lower labeling cost) than random labeling.