Part of the difficulty in understanding this is that we automatically involve other aspects of the imagined scenario in our thinking. The Bayesian approach takes a simplistic view. Here's an example:
In a population of 1000 people, suppose 10 have a disease (ignore how we know that 10 people do....) Suppose we have a test which is correct 90% of the time it claims the disease is present, and also correct 90% of the time it claims the disease is absent. (These two numbers need not be the same, but let's suppose they are.)
Story #1: A new health initiative leads to the entire population being tested. Out of the 10 people with the disease, 9 are correctly identified by the test, while 1 is missed. Out of the 990 people without the disease, 891 (90%) are cleared as healthy, while 99 (10%) are mistakenly labeled as diseased.
Out of the 99 + 9 = 108 people who were tagged as having the disease, only 9 really do. (about 8%). So if we take one of the positive test results at random, that person who tested positive has only an 8% chance of having the disease.
Because the number of healthy people WHO WERE TESTED is so high, more false positives than true positives occurred.
Story #2: The test is expensive and rarely done. Only people who have symptoms suggesting the disease bother to have the test done. So out of our population of 1000, only 30 have the test done, including all 10 who really have the disease. Now, 9 of the 10 people with the disease get positive test results, and 2 of the 20 people WHO WERE TESTED but don't have the disease get positive test results. The chance of having the disease, given that you WERE TESTED and tested positive for the disease, is 9 / 11, or 82%.
The Bayesian analysis describes Story #1 above, but real life is usually more like Story #2. That contributes to the result seeming so counter to intuition.
To more directly address the OP's question: you had the test, and it came out positive. Your question is now "was that positive test result one of the correct positive results, or one of the false positives?" In a situation like Story #1 there are many more false positives than true positives, so you are likely to have gotten a false positive result.