Machine-learning (ML)-assisted seismic interpretation tasks require large quantities of labeled data annotated by expert interpreters, which is a costly and time-consuming process. Whereas existing works to minimize dependence on labeled data assume the data annotation process to already be completed, active learning — a field of ML — works by selecting the most important training samples for the interpreter to annotate in real time simultaneously with the training of the interpretation model itself, resulting in high levels of performance with fewer labeled data samples than otherwise possible. Whereas there exists significant literature on active learning for classification tasks with respect to natural images, there exists very little to no work for dense prediction tasks in geophysics such as interpretation. We have developed a unique and first-of-its-kind active learning framework for seismic facies interpretation using the manifold learning properties of deep autoencoders. By jointly learning representations for supervised and unsupervised tasks and then ranking unlabeled samples by their nearness to the data manifold, we can identify the most relevant training samples to be labeled by the interpreter in each training round. On the popular F3 data set, we obtain close to a 10% point difference in terms of the interpretation accuracy between the proposed method and the baseline with only three fully annotated seismic sections.