It depends on a lot of factors, my unscientific rule of thumb is that if the data doesn't fit in the RAM of the server, then that's when you may start worrying about how the data will be fetched from the disk when you query it (if performance matters).
The problem case is when the data needed to serve an average query is "scattered across the disk". This means that you will need to potentially fetch way more information from the disk (because data is returned by blocks/pages, not by bytes) than necessary to fulfill the query.
A worst case example: say to compute a query, you need to get 5000 rows (with a size of 100 bytes per row, 500KB in net total), if they are "perfectly scattered" (i.e. each row on a different page), you will really need to bring in 5000 pages of 8KB per page (default page size in Postgres) for a total of 40MB. By fetching 80 times more data than needed, you've essentially reduced your throughput by 80x.
Note that the example above assumes that an index (e.g. btree) can be leveraged. The index would point directly to the numerous pages, which is likely still much better compared to doing a full scan. But that index doesn't solve all your problems, only part 1.
It may not be a big deal as bringing 40MB from a disk would go fast, but this will limit 1) the number of concurrent user you'll be able to serve, 2) if you need 100,000 rows instead of 5000, then your query will take longer to process and it may be negatively noticeable by your user.
If you can co-locate the data on the disk (i.e. put them on same or contiguous pages) deterministically, then you would feel much better about your throughput. Cluster Index (or Indexed Views as it's called in SQL Server) is the typical mechanism to sort the data on the disk in RDBMS. MySQL does that by default with the primary key, but not Postgres.