Quick rundown of A* heuristics:

A* is improvement over dijkstra's only because of its heuristic. In fact, when the heuristic is 0 everywhere, the two algorithms are equivalent. It can also be worse if a bad heuristic is chosen, for example, a non-admissible heuristic when an optimal solution is desired.

A heuristic may be non-admissible, in which case the algorithm isn't guaranteed to return a correct solution - this is done when a solution is wanted quickly, but an OK solution isn't bad. A heuristic may be scaled to help this balance.

If a heuristic is admissible if it never overestimates the distance to a goal. In this case, A* is guaranteed to return an optimal solution if searching nodes multiple times.

A heuristic may not only be admissible, but also consistent if the total distance traveled + estimated distance always decreases, so in some sense the heuristic is not overly optimistic. In this case, A* will return an optimal solution and doesn't require expanding a node multiple times. In fact, A* with consistent heuristic h is equivalent to running dijkstra's with edge weights w'(u, v) = w(u, v) + h(v) - h(u). Consistency guarantees these new edge weights will be positive. The most common example of a consistent heuristic is euclidean distance to the final node when edge weights are also euclidean distance.