This is a terminology question: in the context of artificial neural networks, does multi-task learning occur iff the network has more than 1 output?
Having multiple outputs is necessary but not sufficient for multi-task learning.
One task could be a multi-label classification task (e.g. is the input a picture of a human, a cat or a dog?) with one output node per target class. So a network with three outputs is performing a single task.
A second related task could be to predict what time of day the input photo was taken (e.g. morning, afternoon or night). So a multi-task network here could have six outputs defining the two tasks.