When should we apply active learning in testing?

Case 1: I would apply active learning to query a small chunk of samples gradually to label them and my model is being trained during this process. After a certain number of iterations, I have a training dataset with specific performance of the model.

Case 2: I re-train the model from scratch with the training dataset in case 1.

Question 1: do you think the performance of the model will be the same in both cases? why, please?

Question 2: do you think there is a difference to apply active learning to label the testing dataset after case 1 or apply it after case 2 to select more challenging samples to test and retrain the neural network?