It is common practice in seismic data processing to select parameters on a regular grid. For example, first breaks may be picked every kilometer, or denoising parameters may be selected every one hundred shots. This is labour intensive, usually requiring several months of picking and parameter tuning for each seismic dataset. It is also wasteful, as the parameters often do not change substantially between the chosen locations, and may lead to poor results as sudden changes in the best parameter value might be missed. Active learning enables a more efficient use of human effort by instead directing attention to the samples where the appropriate parameter values are most uncertain. It is a data-driven approach that does not require pre-training and is designed to complement human expertise, avoiding difficulties that hamper the use of other machine learning techniques in production. I will discuss the forms of active learning that are relevant for seismic processing and present an application of it on a real dataset.