have you read my above comment on the question?
Sorry for not addressing your main question:
How priority queue can work more efficiently in any data structure, other than heap?
@srestha maam please read this:
First off, How can you deduce this? It is wrong to say anything without workload or number and type of operations we're going to perform, we can only say things, in general case (In priority queues we need to find and extract min or top priority element and since it's a queue there will be a decent number of insertions and deletions so we need an implementation which performs decently on these three operations)
Now which data structure to choose?
Sorted Array:
As I earlier mentioned if we insert an element in somewhere middle we need to shift remaining elements so takes O(n) time, deletion and search are always for top priority element so O(1).
In case of heaps you know we can find top priority element in O(1) as it's root element, for deleting and inserting elements we need to call heapify which takes O(logn) time.
Unsorted Array: Deletion requires O(n) time as element can be anywhere in middle thus require shifting, insertion can be done anywhere, linear search for find.