What is the difference between continual learning and active learning?
Active Learning:

The main hypothesis in active learning is that if a learning algorithm can choose the data it wants to learn from, it can perform better than traditional methods with substantially less data for training.

Active learning (AL) augments the learning process with a model that guides and extends the learning process. Using AL, the agent can ‘actively’ gather labels from multiple sources, or curate its labels better (including rejecting/refining incorrect or incomplete labels), or embed supplementary information on causality or complications among the data that require added effort in resolving difficulties within the process of labeling or in applying existing learning to similar problems.

Continual learning 

Continual learning is the ability of a model to learn continually from a stream of data. In practice, this means supporting the ability of a model to autonomously learn and adapt in production as new data comes in.